Deep.Optimiser-PhyX
Deep.Meta is tackling carbon emissions in the steel industry with an AI-powered Digital Twin – a smart digital replica of the production process that combines physics and machine learning to optimise furnace operations.
By using real-time sensor data and material science, Deep.Optimiser-PhyX more accurately predicts steel slab temperatures and improves scheduling, boosting energy efficiency and significantly cutting emissions. Unlike black-box AI, which can discourage adoption, Deep.Meta’s explainable, physics-based models offer clear reasoning, building trust with users. Founded by experts in metallurgy and machine learning, Deep.Meta is already partnering with global steelmakers and aims to scale through broader industry collaboration.
Deep.Optimiser-PhyX led by Deep.Meta.
“It means a great deal to me and my team to be a Manchester Prize finalist, and that’s because it’s enabled us to have the additional capacity to be able to achieve our goal, which is a 20% reduction of emissions in the steel industry.” – Osas Omoigiade, Deep.Meta
